The present invention generally relates to forming well-segmented objects in three dimensional medical imagery and, in particular, relates to forming well-segmented objects in three dimensional medical imagery using a decision rule or classifier.
Computed Tomography (CT) systems produce volumetric images, providing three-dimensional information of structures internal to the body. This imagery is commonly viewed on film as a collection of many tens of two-dimensional images, also referred to as slices. Each slice is reviewed by the radiologist in the search for abnormalities. Although multiple slices provide more opportunities for a lesion to be detected, the possibility of missing a lesion is also increased due to the increased workload by generating a greater number of individual images per scan. A thoracic CT scan formerly produced approximately thirty sections with the 10-mm collimation that was standard for many years. State-of-the-art multidetector scanners now have collimation as thin as less than 1 mm, and commonly generate more than fifteen times as many section images for radiologists to interpret. With an increase in the number of CT scans being performed for a wide variety of diagnostic and screening purposes compounded by an increasing number of images acquired during each scan, computerized techniques for the automated analysis of CT scans for disease (and especially for lung nodules that may represent lung cancer and colon polyps that may represent colorectal cancer) are quickly becoming a necessity. Additionally, computer-aided detection (CAD) systems are now commercially available and are being developed to assist in challenges of detecting suspicious lesions such as, for example, lung nodules and colon polyps in thoracic imagery.
In initial processing steps, CAD systems typically detect many candidate suspicious areas. In subsequent processing, the initial detections are analyzed to determine whether or not to display a detected region to a user in the final stage. Accurate shape estimates of the initial detections are essential to make good decisions regarding whether or not detections are ultimately displayed.
CAD systems are used to assist radiologists in the detection of suspicious lesions. It is essential for CAD systems to have a reliable estimation of the shape of a lesion in order to make accurate decisions regarding whether or not an initial CAD system detection is ultimately displayed to a user. Therefore, there is a need for a method for accurate shape estimation of nodules or polyps.
Additionally, an object detector locates nearly all the areas of interest within the lungs or colon such as, for example, nodules and polyps. However, the detector Regions of Interest (ROIs) are intended to provide the core of the region of interest, and often underestimate the complete extent of objects that are identified. Some classes of features (e.g., intensity-based features, shape features, etc.) show improved separability with a better estimate of the extent of the ROIs. Therefore, it is beneficial to provide a refined segmentation to the features and classification algorithms in addition to the detection mask.
A common problem during the segmentation is for the region of interest (nodule, polyp, etc.) to be incorrectly attached to an anatomic structure during the step where slices are combined. The incorrect segmentations can lead to poor features, and as a result, can cause otherwise suspicious regions to be rejected during the classification stage.
Often, objects are formed with a simple connectiveness rule. With this rule, voxels are considered part of the same object if the voxels touch. Voxels are the smallest distinguishable box-shaped parts of three-dimensional images. However, when forming an object in three dimensions, simple connectiveness is not sufficient for adjacent slices to be considered part of the same object.
Particularly in the Z dimension in the XYZ coordinate system of non-isotropic CT data, adjacent voxels may actually be several times farther apart than those in the other dimensions. For example, the distance between voxels can be 5 mm or more and the voxels though adjacent can actually represent different structures. Therefore, particularly in these situations, using simple connectiveness is not sufficient since it may incorrectly join voxels that are not part of the same structure.
Additionally, sometimes a mathematical morphological opening is used to break any connections the region of interest has to an anatomic structure. However, this operation is insufficient if the region of interest and the anatomic structure have a high degree of overlap. In addition, previous approaches would often leave the region of interest connected to large anatomic structures. For example, in CT Lung CAD, the nodules would be attached to vessels and bronchial structures and in the CT Colon CAD, the polyps would be attached to folds. Therefore, simply using a mathematical morphological opening is also not enough.
Therefore, there is a need for a decision rule or classifier that examines the CAD detected regions of interest in each CT slice pair to determine whether the detected regions of interest are part of the same object or not. In this way, the detected regions of interest can be correctly separated from the anatomic structures.